From 51f0014fa6be94a089644b200d41a57fd4973b81 Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:45:21 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/README.md b/README.md index 867d655..c2e0d72 100644 --- a/README.md +++ b/README.md @@ -81,3 +81,14 @@ The input data should represent for an audio longer than 2s. - labels: a list of topk predicted labels by model. - scores: a list of scores corresponding to labels, representing for possibility. - vec: a audio embedding generated by model, shape of which is (2048,) + + +# More Resources + +- [Cross-Entropy Loss: Unraveling its Role in Machine Learning - Zilliz blog](https://zilliz.com/learn/Cross-Entropy-Loss-Unraveling-its-Role-in-Machine-Learning): Cross Entropy loss is used for training classification models. +- [Audio Retrieval Based on Milvus - Zilliz blog](https://zilliz.com/blog/audio-retrieval-based-on-milvus): Create an audio retrieval system using Milvus, an open-source vector database. Classify and analyze sound data in real time. +- [Vector Database Use Case: Audio Similarity Search - Zilliz](https://zilliz.com/vector-database-use-cases/audio-similarity-search): Building agile and reliable audio similarity search with Zilliz vector database (fully managed Milvus). +- [Everything You Need to Know About Zero Shot Learning - Zilliz blog](https://zilliz.com/learn/what-is-zero-shot-learning): A comprehensive guide to Zero-Shot Learning, covering its methodologies, its relations with similarity search, and popular Zero-Shot Classification Models. +- [Neural Networks and Embeddings for Language Models - Zilliz blog](https://zilliz.com/learn/Neural-Networks-and-Embeddings-for-Language-Models): Exploring neural network language models, specifically recurrent neural networks, and taking a sneak peek at how embeddings are generated. +- [What is a Generative Adversarial Network? An Easy Guide](https://zilliz.com/glossary/generative-adversarial-networks): Just like we classify animal fossils into domains, kingdoms, and phyla, we classify AI networks, too. At the highest level, we classify AI networks as "discriminative" and "generative." A generative neural network is an AI that creates something new. This differs from a discriminative network, which classifies something that already exists into particular buckets. Kind of like we're doing right now, by bucketing generative adversarial networks (GANs) into appropriate classifications. +So, if you were in a situation where you wanted to use textual tags to create a new visual image, like with Midjourney, you'd use a generative network. However, if you had a giant pile of data that you needed to classify and tag, you'd use a discriminative model. \ No newline at end of file